Field of the Invention
The present invention relates to an information processing apparatus, a control method for the same, and a storage medium.
Description of the Related Art
As one of the technologies for improving the efficiency of a workflow handling paper ledger sheets, image classification utilizing machine learning has been proposed. The image classification utilizing machine learning, which generally has the two processes of learning and classification, is configured to construct a classification rule by learning with a given image group (learning set), and to classify an input image based on the constructed classification rule. In other words, the learning set is learning data used in learning the classification rule for classifying an image that is input into a system.
The application of the image classification utilizing machine learning to the workflow handling paper ledger sheets allows the storage and distribution destinations of a scanned image to be automatically determined, its file name to be automatically generated, and the like. Furthermore, if learning is performed with ledger sheets prepared for each customer, a classification rule that is individually customized can be constructed.
When the property of an image is different in learning and classification, a sufficiently high classification accuracy cannot be obtained. Thus, in general, a large number of images need to be prepared by predicting the images that will be input into classification. A method is known in which a system increases the number of images provided by a user that is utilized in learning in a case when a sufficient number or pattern of images cannot be prepared for a learning set including, for example, a case when learning is performed on site, when the property of an image is changed in classification, or the like.
International application no. WO2010/101186 discloses a method of increasing the number of learning images by performing image processing for mimicking the blur and shake that occur in photographing an image by a camera on a reference image.
A method of merely increasing the number of images photographed by a camera to generate images for a learning set may not, however, be effective as a method of generating a learning set used in classifying an image that is input from a device having a wide variety of characteristics. For example, assume that a camera-equipped mobile terminal photographs an image and inputs it into an information processing apparatus, which classifies the input image according to a classification rule constructed based on a learning set. In this case, the property of an image greatly varies depending on a wide variety of factors when being photographed, such as a distance, inclination, focal distance, light exposure, shutter speed, camera shake, or the like, of the camera-equipped mobile terminal, as well as the characteristic for each device, such as a depth of field, lens characteristic, whether a shake correction function is provided or not, or the like. Therefore, the information processing apparatus needs to generate a learning set suitable for classifying a wide variety of images that vary depending on the characteristic of the camera-equipped mobile terminal.
Moreover, except for a camera, such as a Multi Function Peripheral (MFP) equipped with a scan function, or the like, an image input device may also be used in a workflow handling paper ledger sheets. The MFP inputs a scan image into an information processing apparatus. Then, the information processing apparatus classifies the input scan image. Note that the properties of a scanned image and a camera image are different, and even each property of some scanned images greatly varies depending on a different mode of a scanner and a different standard of skew or positional shift of an image input device.
Thus, since the property of an image greatly varies depending on the type and characteristic of an image input device to be used, it may be useful to prepare a wide variety of images. However, preparing a huge number of images for a learning set by predicting all input images to be utilized in learning a classification rule may make the construction of a classification rule difficult, and may also increase the learning time.